Trustworthy Computer Vision for Real-World Image Processing

Computer vision has achieved remarkable success in controlled research environments, yet its deployment in real-world settings remains constrained by data variability, limited resources, security risks, and trust-related concerns. In practice, vision systems must operate reliably under noise, changing illumination, sensor imperfections, and shifting data distributions while adapting to dynamic environments. These challenges are amplified in safety-critical and socially sensitive contexts, where errors can carry significant consequences. As a result, building robust, secure, and trustworthy real-world image processing systems remains a central priority. Trustworthy Computer Vision for Real-World Image Processing: Robustness, Efficiency, and Deployment addresses the growing gap between laboratory-scale vision models and deployable, dependable systems. This book emphasizes robustness, computational efficiency, explainability, fairness, privacy preservation, and operational reliability as foundational requirements for modern computer vision applications. Rather than focusing solely on model accuracy, the book adopts a holistic perspective that considers trustworthiness across the full system lifecycle. Covering topics such as adversarial robustness and security, predictive maintenance in smart mining, and self-supervised representation learning, this book is an essential academic resource for graduate and doctoral students, computer vision engineers, software engineers, AI governance experts, policymakers, and more.